Most retrieval-augmented apps follow the same path. A vector search returns 30 or 50 passages, and the chat model reads all of them. Some of those passages are close but wrong. Some are right but buried. The model spends tokens on the noise and sometimes answers from the wrong paragraph.

A reranker sits between the search and the chat model. It reads the query and the candidate passages together, scores each one, and returns them in order of relevance. Your code decides how many to keep and where to cut.

Cohere Rerank v5 is now in Microsoft Foundry in two versions, Pro and Fast, and both add a feature called Context Focus. This post covers when each one fits, a table of the trade-offs, and a C# console app that calls both.

If you missed it, I covered the Cohere family in Foundry earlier in Cohere Command A+ Lands in Microsoft Foundry .…